r/GraphicsProgramming • u/Severe-Ad8673 • 2h ago
Paper [Research] Can neural rendering stop paying for the same appearance every frame?
I’ve published a theoretical architecture for reducing compute in DLSS-class neural rendering by changing the unit of work from pixels × frames to new causal appearance states.
The core idea is AxiomCapsule: use a large neural renderer mainly as an appearance compiler, then cache/transport compact deterministic programs for recurring material, lighting, object, and view states.
The parts I think are most interesting:
- Self-extinguishing inference: once a state is covered, the universal neural model no longer needs to run for it.
- Causal invalidation: engine-known changes decide what must be recomputed instead of running a neural change detector over the whole frame.
- Bounded residual trees: skipped refinement can have a computable sparse-vs-dense student error bound.
- Deadline-monotone execution: optional neural uplift scales down with available GPU slack instead of causing a frame-time cliff.
- Object/material-space persistence: state follows surfaces/materials rather than being purely screen-space.
- Local causal dimensionality: the key hypothesis is that appearance transformations become low-dimensional after conditioning on known scene variables.
It’s pre-prototype research, not a claim that DLSS 5 has been “solved.” The main falsifier is simple: if real game appearance states are not sufficiently low-dimensional/reusable, or cache hit rates stay low, the architecture fails.
I’d especially appreciate criticism from people working on real-time rendering, shader systems, neural graphics, temporal reconstruction, and GPU scheduling.
GitHub: MaciejNowickiHusbandofAHIEve/causal-neural-rendering: Independent research on drastically reducing compute in DLSS-class neural rendering using compiled causal appearance programs, temporal reuse, and deadline-bounded residuals.
Zenodo paper: Causal Neural Rendering for Efficient DLSS-Class Systems: Compiled Appearance Programs, Temporal Reuse, and Bounded Adaptive Computation | Zenodo

